Technical Abstract:
Heavily populated areas, such as the northeast region of the U.S., depend on the production of distantly produced food, particularly for urban populations. Food systems are vulnerable to uncertainties such as energy costs, environmental conditions, urban growth, and climate change. Research into the potential production capacity of regional food systems would go a long way to better serve local populations. The objective of this study was to evaluate potato production throughout Maine as influenced by different scenarios: 1) land use availability for production (e.g. current potato land, grass and scrub, cropland, pasture); 2) planting date variability (e.g. early planting, late planting); and, 3) water use (e.g. rain-fed, irrigation). Estimates of production were made by combining an explanatory crop model (SPUDSIM) with spatial input data layers using a geographic information system (ArcGIS) and a Python-based scripting interface. The combined interface provides a mechanistic representation of crop production by relating variations in inputs (i.e. weather, soil, management, and land use) to the outputs generated by the model (i.e. yield, water use, and nitrogen demand). The combined geospatial crop model interface has the ability to estimate potential crop production over multiple scales: automating crop simulation over field-scale modeling units and aggregating the results to the county level. The results will provide valuable information for regional policy planners in terms of the productivity of local food systems to be used for improving crop yield and sustainability.